So far, because of the complexity of each application, the ranking methods of documents and contents are various and many in numbers. Currently the ranking and summarization relies mostly on superficial indication of importance of a composition or its components usually by utilizing the signs of the human composer judgment of importance.
For example, at the sentence ranking level, the document summarization method disclosed in the published US patent application 2006/0206806 A1, scores a sentence for inclusion in the summary by evaluating the weighted score of individual words and type of the sentences. The score of the sentences and the individual words is determined by the type of sentence such as being a title sentence, sub-title, or sentence location in a paragraph, plus word features such as the lengths of the word, part of speech type of the word, and word syntax function in a sentence. At the webpage level the PageRank ranking method uses the link referral centrality value of WebPages plus other syntactic and representation of each page such as the frequency of the keyword, its location, font size, and the like in the webpage. These importance factors are basically based on the perceived importance of the creator of the compositions rather than the real substantive value of the compositions itself and therefore these methods can be susceptible to manipulations. More advanced ranking method of textual compositions are based on natural language processing of words and word disambiguation etc. which are very process intensive and still not yielding a satisfactory results.
Moreover these methods of ranking for different level usually involves a huge amount of text processing that is very process intensive and cannot be easily scale up (due to the application specific ranking methods, such as natural language investigations methods) to achieve a fair ranking stand from a very large number of existing and future collections of compositions. However it is desirable to have a ranking method that select the components and the compositions based on the intrinsic value of composition or the partitions thereof in a fair comparison to a huge number of other compositions.
More importantly, at this age of information overload and overabundance, knowledge and information users desire to have the right information or the true knowledge in their fingertips rather than searching through a countless sets of WebPages returned by search engines. Therefore, summarization and distillation of single and multi-composition, or finding the answers to questions from a single or a set of compositions, as well as searching for compositions having the highest substance from collections of compositions, e.g. determining an important gene from a genome, are useful and highly desirable services for users of different groups, having different goals in mind, while exploring for knowledge acquisitioning, and seeking information related to their topic of interest.
Therefore there is a need in the art for unified, systematic, and process efficient ranking methods and the associated systems, which can cover the rankings at all the levels and all types of compositions. More importantly the results should be based on the real substance, semantic and intrinsic knowledge value of the compositions in fair comparisons to other large number of competing compositions for selection of the most appropriate answer to a user request for information or knowledge.